• Abdalla, S., L. Isaksen, P. Janssen, and N. Wedi, 2013: Effective spectral resolution of ECMWF atmospheric forecast models. ECMWF Newsletter, No. 137, ECMWF, Reading, United Kingdom, 19–22, https://doi.org/10.21957/rue4o7ac, https://www.ecmwf.int/en/elibrary/17358-effective-spectral-resolution-ecmwf-atmospheric-forecast-models.

  • Ancell, B. C., 2016: Improving high-impact forecasts through sensitivity-based ensemble subsets: Demonstration and initial tests. Wea. Forecasting, 31, 10191036, https://doi.org/10.1175/WAF-D-15-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berger, V. W., and Y. Zhou, 2005: Binomial distribution: Estimating and testing parameters. Encyclopedia of Statistics in Behavioral Science, B. Everitt and D. Howell, Eds., Wiley, https://doi.org/10.1002/0470013192.bsa051.

    • Crossref
    • Export Citation
  • Black, T., H. M. H. Juang, and M. Iredell, 2009: The NOAA Environmental Modeling System at NCEP. 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A.6, https://ams.confex.com/ams/23WAF19NWP/techprogram/paper_154223.htm.

  • Blaylock, B. K., J. D. Horel, and S. T. Liston, 2017: Cloud archiving and data mining of high-resolution rapid refresh forecast model output. Comput. Geosci., 109, 4350, https://doi.org/10.1016/j.cageo.2017.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Candille, G., 2009: The multiensemble approach: The NAEFS example. Mon. Wea. Rev., 137, 16551665, https://doi.org/10.1175/2008MWR2682.1.

  • Clark, A. J., and et al. , 2012: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Soc., 93, 5574, https://doi.org/10.1175/BAMS-D-11-00040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and et al. , 2018: The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Bull. Amer. Meteor. Soc., 99, 14331448, https://doi.org/10.1175/BAMS-D-16-0309.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Done, J., C. A. Davis, and M. Weisman, 2004: The next generation of NWP: Explicit forecasts of convection using the Weather Research and Forecasting (WRF) model. Atmos. Sci. Lett., 5, 110117, https://doi.org/10.1002/asl.72.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dong, L., and F. Zhang, 2016: OBEST: An observation-based ensemble subsetting technique for tropical cyclone track prediction. Wea. Forecasting, 31, 5770, https://doi.org/10.1175/WAF-D-15-0056.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., and M. S. Tracton, 2001: Implementation of a real-time short-range ensemble forecasting system at NCEP: An update. Preprints, Ninth Conf. on Mesoscale Processes, Fort Lauderdale, FL, Amer. Meteor. Soc., 355–356.

  • Ebert, E. E., 2001: Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129, 24612480, https://doi.org/10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fosser, G., E. Kendon, S. Chan, A. Lock, N. Roberts, and M. Bush, 2019: Optimal configuration and resolution for the first convection-permitting ensemble of climate projections over the United Kingdom. Int. J. Climatol., https://doi.org/10.1002/joc.6415, in press.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., and A. E. Raftery, 2007: Strictly proper scoring rules, prediction, and estimation. J. Amer. Stat. Assoc., 102, 359378, https://doi.org/10.1198/016214506000001437.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gowan, T. M., W. J. Steenburgh, and C. S. Schwartz, 2018a: Validation of mountain precipitation forecasts from the convection-permitting NCAR ensemble and operational forecast systems over the western United States. Wea. Forecasting, 33, 739765, https://doi.org/10.1175/WAF-D-17-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gowan, T. M., W. J. Steenburgh, and C. S. Schwartz, 2018b: Validation of mountain precipitation forecasts from the convection-permitting NCAR ensemble and operational forecast systems over the western United States. Wea. Forecasting, 33, 739765, https://doi.org/10.1175/WAF-D-17-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. W., 1999: First- and second-order conservative remapping schemes for grids in spherical coordinates. Mon. Wea. Rev., 127, 22042210, https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., S. J. Weiss, J. J. Levit, M. E. Baldwin, and D. R. Bright, 2006: Examination of convection-allowing configurations of the WRF model for the prediction of severe convective weather: The SPC/NSSL spring program 2004. Wea. Forecasting, 21, 167181, https://doi.org/10.1175/WAF906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., and et al. , 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection-allowing NWP. Wea. Forecasting, 23, 931952, https://doi.org/10.1175/WAF2007106.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klaver, R., R. Haarsma, P. L. Vidale, and W. Hazeleger, 2020: Effective resolution in high resolution global atmospheric models for climate studies. Atmos. Sci. Lett., https://doi.org/10.1002/asl.952, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Köppen, W., and R. Geiger, Eds., 1936: Das geographische System der Klimate. Handbuch der Klimatologie, Vol. 1, Borntraeger, 1–44.

  • Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel, 2006: World map of the Köppen-Geiger climate classification updated. Meteor. Z., 15, 259263, https://doi.org/10.1127/0941-2948/2006/0130.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, https://doi.org/10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA/NCEP/NWS, 2015a: NCEP GFS 0.25 degree global forecast grids historical archive. National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed August 2019, https://doi.org/10.5065/D65D8PWK.

    • Crossref
    • Export Citation
  • NOAA/NCEP/NWS, 2015b: NCEP North American Mesoscale (NAM) 12 km analysis. National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed August 2019, https://doi.org/10.5065/G4RC-1N91.

    • Crossref
    • Export Citation
  • Palmer, T. N., 2002: The economic value of ensemble forecasts as a tool for risk assessment: From days to decades. Quart. J. Roy. Meteor. Soc., 128, 747774, https://doi.org/10.1256/0035900021643593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Parrish, D. F., and J. C. Derber, 1992: The National Meteorological Center’s spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 17471763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pavia, E. G., F. Graef, and R. Fuentes-Franco, 2016: Recent ENSO–PDO precipitation relationships in the Mediterranean California border region. Atmos. Sci. Lett., 17, 280285, https://doi.org/10.1002/asl.656.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peckham, S. E., T. G. Smirnova, S. G. Benjamin, J. M. Brown, and J. S. Kenyon, 2016: Implementation of a digital filter initialization in the WRF Model and its application in the Rapid Refresh. Mon. Wea. Rev., 144, 99106, https://doi.org/10.1175/MWR-D-15-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perconti, P., Ed., 2017: History of the U.S. Army research Laboratory. ARL, Government Printing Office, 75 pp., https://www.arl.army.mil/wp-content/uploads/2019/10/History-of-the-U.S.-Army-Research-Laboratory.pdf.

  • Qi, L., H. Yu, and P. Chen, 2013: Selective ensemble-mean technique for tropical cyclone track forecast by using ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 140, 805813, https://doi.org/10.1002/qj.2196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robert, D., R. Brian, S. Jeffrey, K. David, and C. Huaqing, 2014: A WRF-based mixed variational and nudging assimilation scheme for US army convection-scale nowcasting. The World Weather Open Science Conf., Las Cruces, NM, WMO, 1–18, https://www.wmo.int/pages/prog/arep/wwrp/new/wwosc/Presentations_wwosc_17082014.html.

  • Rubel, F., and M. Kottek, 2010: Observed and projected climate shifts 1901-2100 depicted by world maps of the Köppen-Geiger climate classification. Meteor. Z., 19, 135141, https://doi.org/10.1127/0941-2948/2010/0430.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2011: NCEP climate forecast system version 2 (CFSv2) 6-hourly products. National Center for Atmospheric Research, Computational and Information Systems Laboratory, accessed August 2019, https://doi.org/10.5065/D61C1TXF.

    • Crossref
    • Export Citation
  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570575, https://doi.org/10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and R. A. Sobash, 2017: Generating probabilistic forecasts from convection-allowing ensembles using neighborhood approaches: A review and recommendations. Mon. Wea. Rev., 145, 33973418, https://doi.org/10.1175/MWR-D-16-0400.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, C. S., and et al. , 2009: Next-day convection-allowing WRF Model guidance: A second look at 2-km versus 4-km grid spacing. Mon. Wea. Rev., 137, 33513372, https://doi.org/10.1175/2009MWR2924.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, https://doi.org/10.1175/MWR2830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., http://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Tapiador, F. J., R. Roca, A. D. Genio, B. Dewitte, W. Petersen, and F. Zhang, 2019: Is precipitation a good metric for model performance? Bull. Amer. Meteor. Soc., 100, 223233, https://doi.org/10.1175/BAMS-D-17-0218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Theis, S. E., A. Hense, and U. Damrath, 2005: Probabilistic precipitation forecasts from a deterministic model: A pragmatic approach. Meteor. Appl., 12, 257268, https://doi.org/10.1017/S1350482705001763.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thielen, J., K. Bogner, F. Pappenberger, M. Kalas, M. del Medico, and A. de Roo, 2009: Monthly-, medium-, and short-range flood warning: Testing the limits of predictability. Meteor. Appl., 16, 7790, https://doi.org/10.1002/met.140.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H., 2014: Descriptions of the major modeling systems operated at NOAA/NWS/NCEP. NOAA Tech. Rep., 70 pp.

  • Weidle, F., Y. Wang, and G. Smet, 2016: On the impact of the choice of global ensemble in forcing a regional ensemble system. Wea. Forecasting, 31, 515530, https://doi.org/10.1175/WAF-D-15-0102.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW Model. Wea. Forecasting, 23, 407437, https://doi.org/10.1175/2007WAF2007005.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, H., and W. A. Gallus, 2016: An evaluation of QPF from the WRF, NAM, and GFS models using multiple verification methods over a small domain. Wea. Forecasting, 31, 13631379, https://doi.org/10.1175/WAF-D-16-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Local Quantitative Precipitation Forecast with Minimal Data Requirement—An Ensemble Approach

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Abstract

Operational weather forecasts are routinely performed at convection-allowing resolutions, and thus these forecasts generate weather features that appear to be realistic. However, at times the comparison of the forecast to observations is less favorable, particularly at grid scales. This lack of skill is partly due to the chaotic system underlying the weather. Another problem is that it is impossible to evaluate the risk of making decisions based on deterministic forecasts. However, running global high-resolution ensembles involves substantial computational resources. A 555-m resolution WRF ensemble based on stochastic perturbations of a deterministic forecast of the North American Mesoscale model was created. Observations are used to constrain the ensemble and improve the skill. This method increases the skill for forecasting 60-h accumulated precipitation in five standard, statistical metrics: bias, false alarm ratio, threat score, probability of detection, and success ratio. Furthermore, the ensemble continuous ranked probability score (CRPS) will be compared to a poor man’s ensemble. The forecast error is generally smaller in more than 70% of the case studies performed when compared to nine deterministic model forecasts. The ensemble-enhanced mesoscale system presented can help to determine the most likely scenario without the significant computational requirement of global ensembles and is expected to be useful when global high-resolution ensembles are not available.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-19-0077.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Markus Gross, mgross@cicese.mx

Abstract

Operational weather forecasts are routinely performed at convection-allowing resolutions, and thus these forecasts generate weather features that appear to be realistic. However, at times the comparison of the forecast to observations is less favorable, particularly at grid scales. This lack of skill is partly due to the chaotic system underlying the weather. Another problem is that it is impossible to evaluate the risk of making decisions based on deterministic forecasts. However, running global high-resolution ensembles involves substantial computational resources. A 555-m resolution WRF ensemble based on stochastic perturbations of a deterministic forecast of the North American Mesoscale model was created. Observations are used to constrain the ensemble and improve the skill. This method increases the skill for forecasting 60-h accumulated precipitation in five standard, statistical metrics: bias, false alarm ratio, threat score, probability of detection, and success ratio. Furthermore, the ensemble continuous ranked probability score (CRPS) will be compared to a poor man’s ensemble. The forecast error is generally smaller in more than 70% of the case studies performed when compared to nine deterministic model forecasts. The ensemble-enhanced mesoscale system presented can help to determine the most likely scenario without the significant computational requirement of global ensembles and is expected to be useful when global high-resolution ensembles are not available.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-19-0077.s1.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Markus Gross, mgross@cicese.mx

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